Reconstruction of three dimensional model of an object compensating for object orientation changes between surface or slice scans

ABSTRACT

Techniques for creating a three dimensional model of an object and eliminate artifacts due to changes in orientation of the object between successive scans. Such techniques may be applied to line scans and slice scans of an object.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation in part of a copending U.S. patent application Ser. No. 15/950,233 filed Apr. 11, 2018, which was a continuation in part of U.S. patent application Ser. No. 15/345,637 filed Nov. 8, 2016, and now issued as U.S. Pat. No. 9,972,101 by Robert P. Bishop et al., which was a continuation of U.S. patent application Ser. No. 15/213,709, which was filed on Jul. 19, 2016, and now issued as U.S. Pat. No. 9,489,753 by Robert P. Bishop et al. The entire contents of each of the above referenced patent applications are hereby incorporated by reference.

BACKGROUND Technical Field

This application relates to imaging of objects in three dimensions, and more particularly to compensating for motion blur.

Background Information

There are numerous scanning technologies in which multiple line scans or image slices are combined to reconstruct the three dimensional shape of an object. Such scanning technologies include: (a) Laser triangulation, to create surface line scans of an object, as used for example in surface CAD-CAM reconstruction applications, and (b) Optical Coherent Tomography (OCT), Magnetic Resonance Imaging (MRI), Ultrasound Imaging, Radar imaging, and Sonar Imaging, to create image slice scans of an object for internal object 3D reconstruction applications. In all of these applications multiple individual scan lines or image slices must be recombined, with precise knowledge of the spatial relationship between each scan line or image slice, to accurately reconstruct the object in three dimensional space.

Examples of such image slices are shown in FIGS. 1 to 3. FIG. 1 shows example MRI image slices 100 of the human brain. FIG. 2 illustrates a single OCT scan slice 200 of the human eye. FIG. 3 is an example single ultrasound scan slice 300 of the human abdominal area.

SUMMARY

Motion of the object during or between acquisition of the multiple scans or slices introduces severe artifacts in the reconstructed three dimensional image. For example, consider the stationary stair case object 400 shown in FIG. 4 and the flat object 500 in vertical motion shown in FIG. 5. When the stationary stair case object of FIG. 4 is scanned using any of the above listed technologies the measurement device will correctly indicate that the object has a step with a height difference of “h”. However, if the flat object 500 shown in FIG. 5 moves a vertical distance h between successive scans, the measurement device will incorrectly indicate that there exists a step in the surface of height “h” when the surface 502 is perfectly flat but in motion as shown by arrow 510. Thus the object 400 of FIG. 4 may not be distinguished from the object 500 in FIG. 5.

FIGS. 6A and 6B are images of a human eye being scanned. FIGS. 6A and 6B show the x,y path of a scan line at different spatial positions during the scan. FIG. 6A is a first image 600 showing scan 610 taken at time t=0. The “ring” 620 around the pupil is an artifact of reflection of a white light illuminator in the scanner. FIG. 6B is the 25^(th) image 650 showing scan 660, taken at time t=0.65 seconds. Image 700 in FIG. 7 is the superposition of FIGS. 6A and 6B showing how much the eye 730 moved in the x and y directions due to random eye motion within the 0.65 second scanning period.

While FIG. 7 shows how much the eye moves in a plane parallel to the camera, the camera cannot detect vertical displacement perpendicular to the camera in the z direction. In fact, if the vertical displacement lies within the depth of field of the camera optics, the vertical motion may not even be detectable, because the image will remain in focus. FIG. 8 shows an example of this situation; since the object moves along the same axis 810 as the camera lens 805 depth of field 820, motion of the object 800 may not be evident between successive scans.

A summary of various embodiments is provided below. Detailed explanations of the various embodiments with reference figures is presented in the section entitled “Detailed Description of an Illustrative Embodiment”.

In one example embodiment, these problems are overcome by an apparatus and/or method that includes a camera directed along an imaging axis. The camera generates a sequence of two-dimensional images of the object including a distinct visual feature. A scanner also generates multiple scans of the object, each scan including a plurality of sampled measurements of the object. Each scan should have a corresponding two dimensional image. Some type of fixed or otherwise determined spatial relationship is maintained between the scanner and the camera.

A controller, such as a programmable digital computer or digital signal processor, is connected to control the camera and the scanner and to process their outputs. The controller controls the scanner to produce a plurality of scan lines, each scan line including a plurality of sampled measurements. The scanner is furthermore controlled such that each of the scan lines intersects at least one other scan line in an image plane observed by the camera, to thereby create a path from any single scan line to any other scan line in the image plane. The controller also captures an image of the object including the distinct visual feature from the camera for each of the plurality of scan lines, thereby capturing a corresponding image for each scan line. The captured images are then aligned by overlaying the distinct visual feature, to thereby also align the corresponding scan lines in the image plane by a corresponding amount.

The individual sampled measurements for each given scan line are then connected to produce a continuous representation of each scan line in three dimensional space. For at least one set of two scan lines, the controller then locates a point in the image plane where the continuous representations of the two scan lines cross, and then moves at least one scan line along the imaging axis in a plane perpendicular to the image plane. This results in the two continuous scan line representations intersecting at a common point in three dimensional space, thereby compensating for movement of the object between successive scans of the scanner.

A three dimensional representation of the object may then be generated from two or more of the intersecting continuous scan line representations.

In some implementations, the captured image may be a two-dimensional image of a surface of the object containing the distinct visual feature.

The scanner may be of different types. In one implementation, the scanner is a surface height scanner and the sampled measurements each correspond to a height determined from a distance between a reference point to the object at a selected position along the scan line.

In an other implementation, the scanner is a slice scanner, and the sampled measurements are slice scans. In this implementation, a surface of the object is extracted from each slice scan. Example slice scanners include OCT, CAT, Ultrasound, MIR, sonar, or radar.

In a preferred embodiment, a time to acquire the sampled measurements from the scanner is sufficiently small such that motion blur occurring during the sampling of a scan line or slice scan can be ignored.

In other aspects, an apparatus and/or method generates a three dimensional representation of an object using a scanner for generating slice scans. Each slice scan includes an array of samples in two dimensions, with each sample representing an intensity of a detected physical property of the object. A controller, such as a programmable digital computer or digital signal processor, is connected to the scanner. The controller controls the scanner to produce a plurality of slice scans, with the slice scans being arranged such that each slice scan intersects at least two other slice scans, even as the object moves between successive slice scans. The controller also controls the slice scans to intersect such that a continuous path exists from each slice scan to any other slice scan. The controller processes the output of the scanner to locate a set of common intensity values that a given scan slice has in common with a selected intersecting slice, and then aligns the intensity values that the given slice and selected intersecting slice have in common. This then fixes a position for the given scan slice and selected intersecting slice in a three dimensional representation of the object.

In this embodiment, the controller may further process the output of the scanner to locate two or more sets of common intensity values that the given slice has in common with the selected slice. Using relative position information for the given slice with respect to a third slice, the controller may then determine which one of the two or more sets of common intensity values to use to fix the position of the given scan slice and the selected intersecting slice.

In other optional implementations, the control may further limit a size and location of a search area within which to locate intersecting slices, using (a) slice scan angle and slice position and (b) a known maximum rate of object motion between scan slices.

In this implementation, a time to acquire the samples may also be sufficiently small such that motion blur occurring during successive samples of a slice scan can be ignored.

BRIEF DESCRIPTION OF THE DRAWINGS

The description below refers to the accompanying drawings, of which:

FIG. 1 is a set of MRI image slices of a human brain;

FIG. 2 is a single OCT scan of a human eye;

FIG. 3 is a single ultrasound scan of a human abdomen;

FIG. 4 is an example situation where two scans are taken of a stationary staircase object;

FIG. 5 is an example situation where two scans are taken of a moving flat object;

FIG. 6A is an image of an eye taken during a first scan (at time t=0 seconds);

FIG. 6B is an image of the eye taken during a subsequent scan (at time t=0.65 seconds);

FIG. 7 is a superposition of the FIGS. 6A and 6B images;

FIG. 8 shows how motion parallel to the depth of field may not be discernable by comparing images;

FIG. 9 is an example implementation;

FIGS. 10A to 10D illustrate a sequence of steps to perform a three dimensional (3D) reconstruction of line-scanned objects with motion blur eliminated;

FIGS. 11A through 11C are an example of using multiple alignment features for aligning images and scans.

FIG. 12A is an example scan pattern prior to alignment;

FIG. 12B is the scan pattern after alignment;

FIG. 13A shows a measured z height surface profile for a single scan line;

FIG. 13B illustrates a single scan line that produces the z height profile of FIG. 13A;

FIG. 14 is a superposition of 120 unaligned scans/images with blurred image features;

FIG. 15 is a superposition of the same 120 scans/images after alignment with sharply defined image features;

FIG. 16 is a three dimensional model of a human eye;

FIGS. 17A, 17B and 17C are representations of rotational views of the model of FIG. 16;

FIG. 18A is an image of an eye;

FIG. 18B is an OCT scan slice of the same eye as in FIG. 18A;

FIG. 19 shows how the top surface of the eye can be identified;

FIG. 20 shows a second implementation;

FIGS. 21A through 21D illustrate a method for 3D reconstruction of a slice scanned object with elimination of motion blur;

FIGS. 22A to 22B-4 show an OCT image plane with different rotations;

FIGS. 22C through 22J-2 show OCT images for different objects;

FIGS. 23A through 23C show orthogonal and non-orthogonal scans;

FIGS. 23D-1 through 23D-3 show alignment features with respect to a target;

FIG. 24 represents a slice scanner without a camera;

FIG. 25A shows intersecting slice scans when the object does not move during the scan.

FIG. 25B shows the intersecting scan slices when the object does move during the scan;

FIG. 25C illustrates how the slices may share a common set of intensity values;

FIG. 26A illustrates a spatial region over which the slices must intersect in 3 dimensions;

FIG. 26B illustrates a spatial region over which the slices must intersect in 2 dimensions;

FIG. 27 shows a case of duplicate intensity vectors where it is not yet known where the slices are connected;

FIGS. 28, 29 and 30 show three slices and their vector intensity positions;

FIG. 31 shows image slice 1 and image slice 2 joined at a common vector;

FIG. 32 shows possible positions of image slice 3 and radius D₃;

FIG. 33 is a final reconstructed model;

FIG. 34A is a mechanical scan pattern and FIG. 34B the corresponding intersecting slices;

FIG. 34C shows how a larger region can be scanned by four smaller intersecting regions;

FIG. 35 shows an object being scanned from different angles;

FIG. 36 is an example of taking images for different gazes of the eye;

FIG. 37 is a head on view of an object with two alignment features;

FIG. 38 shows how to locate alignment features in object models;

FIG. 39 illustrates preparation for translation correction;

FIG. 40 illustrates a spatial relationship of the two models after translation;

FIG. 41 illustrates preparation for rotation correction;

FIG. 42 shows rotation correction;

FIGS. 43A through 43I show subsections with different orientations;

FIGS. 44A and 44B illustrate, respectively, the result of scanning without and then with the correction techniques;

FIGS. 45A and 45B are OCT slice scans of an injured cornea; and

FIG. 46 shows the error of scans stitched at the limbus.

DETAILED DESCRIPTION OF AN ILLUSTRATIVE EMBODIMENT

Described herein are various apparatus and methods for removing three dimensional (3D) motion blur in line scan and slice scan images. The techniques enable the 3D shape of an object to be reconstructed without adverse effects due to motion in x, y, and z directions as the object is scanned.

Implementation 1

Three Dimensional Reconstruction of Line Scanned Objects and Elimination of Motion Blur

This first implementation (Implementation 1) uses a line scan device to obtain a 3D model. Referring to FIG. 9, this requires the following equipment and operations:

1. A single two dimensional (2D) camera 920 to take a sequence of images of object 970 which includes at least one feature on the surface 960 of object 970. The camera may be a television camera or other digital camera capable of capturing a sequence of images.

2. A scanner 910 that measures at least the distance to the surface of the object 970 along a sequence of line scans (e.g., 960) (such as a triangulation sensor).

3. The scanner is controlled by a controller 940 such that scan lines must intersect in such a way as to create a path to travel from any single scan line to any other scan line.

4. A determined spatial relationship exists between the camera 920 and line scan 910 device. The determined spatial relationship may be fixed by mounting the camera and scanner on a shared platform. In other arrangements, the scanner and camera may move with respect to one another, but the spacing between them follows a known pattern.

5. The time required to scan a single line is sufficiently fast such that no motion blur occurs during a single scan line acquisition time or the motion blur is significantly small such that it can be ignored over the line scan time.

6. A digital signal processor and/or computer 950 is programmed to process the output of the camera 920 and the output of the scanner 910, and interacts with controller 940, to control the operation of the camera, the scanner, and to perform the method steps below. The DSP/computer 950 may further include storage 952, display 954 and other peripheral components known in the art.

Given the above requirements are satisfied, the surface 960 of object 970 can be reconstructed in three dimensional space by a line scan device. Please refer to the method steps illustrated in FIGS. 10A to 10D as follows.

Step 1 (FIG. 10A): Take at least two surface line scans, scan 1 (1010-1) and scan P (1010-P), and also capture a corresponding image of the surface with the camera, simultaneously with each scan; store the line scan and camera image data for further processing. Select at least one feature (1020-1, 1020-P) in the surface image of the object to be used for x and y alignment of the surface height line scans. This example uses a “+” sign as the alignment feature.

Step 2A (FIG. 10B): Move one surface image relative to the other surface image in x and y to overlay the alignment feature (1020-1, 1020-P) present in both images. Also move the surface height line scan an identical amount as the surface image to maintain at all times the precise spatial relationship between the alignment feature in the surface image and each surface height line scan.

Step 2B (FIG. 10B): Mathematically connect the individual sampled height data points in each line scan as shown by the bold lines on the top surface of the object. One way to connect the points is to fit a polynomial to the sampled data values, 1010-1 and 1010-P respectively. Locate the x, y coordinate on the top surface that is common to both line scans 1(1010-1) and P (1010-P). This is the coordinate where the two scans intersect in the x-y plane, as indicated by dashed vertical line V1.

Step 3 (FIG. 10B): Since the common x, y data point lies on the surface of the object, and there is one and only one top surface, both surface height line scans must have the same z height value relative to the object where the two scans intersect in the x, y plane as indicated by vertical line V1 in Step 2B. Therefore line scan P (1010-P) can be and is connected to line scan 1 (1010-1) at this surface x, y, z coordinate.

Step 4 (FIG. 10C): For each subsequent line scan 1010-N acquired of the object (referred to as scan line N) the alignment feature 1020-N is located in the accompanying surface image. This is true whether the object is moving or stationary as it is being scanned.

Step 5 (FIG. 10D): Mathematically connect the individual sampled data points in scan line N using the same procedure described in step 2B. Find which previously processed line scan number, referred to as line scan P, that line scan N intersects. Locate the x, y coordinate that is common to line scan N and P. This is the x, y coordinate where the two scans intersect in the x-y plane, as indicated by dashed vertical line V2.

Step 6 (FIG. 10D): At the intersecting x, y surface coordinate common to line scans N and P (indicted by vertical line V2 in FIG. 10D (Step 5)), connect the line scans in the vertical direction such that line scan N will now have the same x, y, z coordinate value as line scan P where the two line scans intersect.

Continue the operations in steps 4, 5, and 6 until all intersecting scan lines have been connected in the vertical Z height axis.

While FIGS. 10A through 10D illustrate the use of one alignment feature in the camera image to spatially align the object in the x,y camera plane between two scans; it may be necessary or useful to use multiple features on the object surface to achieve alignment, especially if a single feature is not present in all camera images due to object motion. FIGS. 11A,11B, and 11C show how multiple alignment features between consecutive images can be used for alignment. As an example, the “+” (plus sign 1110) is only present in the camera image of FIG. 11A. Both the “+” and “*” (asterisk 1111) are present in camera image of FIG. 11B and only the “*” asterisk is present in the camera image of FIG. 11C. In this example the “+” sign can be used to align FIG. 11A to 11B and the “*” asterisk can be used to align FIG. 11B to FIG. 11C. This by default aligns FIG. 11A to FIG. 11C even though they do not share a common feature. In this manner all images and scans can be aligned to each other in the x,y camera image plane. In general any one image can be aligned to any other image either by directly containing a feature common to both images or by finding pairs of common features in intermediate images linking the photos together as shown in FIGS. 11A, 11B, and 11C.

It is noted that methods for alignment of two images in the x,y plane is well known in the art. The novelty of this implementation is believed to be the complete method of line scan alignment in the z axis, to compensate for object motion between line scans.

FIG. 12A is an example 1201 of multiple intersecting line scans of a human eye. The corresponding surface images are overlaid. This is the pattern prior to the x,y alignment needed to compensate for the eye motion shown in FIG. 7. FIG. 12B illustrates how the scans will be positioned relative to each other after alignment in the x, y plane 1202 using the aligned surface images per the method steps as illustrated in FIGS. 10A through 10D.

FIG. 13A shows an example measured Z height surface profile along an example single scan line 1300; the corresponding image is in FIG. 13B.

FIG. 14 is the superposition 1400 of one-hundred twenty (120) unaligned scan images of an eye acquired over a 3 second total scan period with each image having a corresponding Z height scan line profile. FIG. 14 shows the actual scan pattern 1410 and density of data points over the pupil of the eye. Note that the features in FIG. 14 are blurred. FIG. 15 shows the superposition 1500 of the 120 scans/images 1510 after alignment eliminating motion blur in the x and y directions using the methods described in FIGS. 10A to 10D. Note that the features in FIG. 15 are sharp.

In another scan of the eye, the 120 intersecting scan lines shown in FIG. 14 were radially extended past the eyelids and the three dimensional model of the eye shown in FIG. 16 was created using the reconstruction methods described in FIGS. 10A to 10D, to compensate for motion in the Z direction.

The three dimensional model can be rotated on the computer screen 954 (See FIG. 9) to display the model at different desired viewing angles 1701, 1702, 1703 as shown in FIGS. 17A to 17C.

Implementation 2

Three Dimensional Reconstruction from Slice Scans of an Object and Elimination of Motion Blur

This implementation describes methods for three dimensional reconstruction from slice scans of an object and elimination of motion blur. Slice scans create image slices of an object. Scanning technologies such as: Optical Coherent Tomography (OCT), Magnetic Resonance Imaging (MRI), Ultrasound Imaging, Radar imaging, and Sonar Imaging may be used to create image slices of the object. For each of these applications, the multiple individual image slices must be recombined, with precise knowledge of the spatial relationship between each slice and the object being scanned, to reconstruct the object in three dimensional space.

To describe this novel 3D object reconstruction algorithm, (Implementation 2) we will use OCT scans of the eye as an example. FIG. 18A is an image 1800 of an eye showing the relative position of an example OCT scan slice across the eye; and FIG. 18B is an example Optical Coherent Tomography (OCT) image slice 1810 of the eye.

The top surface of the object (eye) can be easily identified in each image slice 1900 as shown in FIG. 19 such as by identifying the “first” high contrast boundary 1910 encountered when moving “down” in the z axis direction. Once the top surface is identified the object can be reconstructed in three dimensional space using the method steps described in FIGS. 21A through 21D, given the image slices meet the criteria set forth below.

Implementation 2 uses a slice scan device to obtain a 3D model. This approach requires the following equipment (as depicted in FIG. 20) and operations:

1. A single two dimensional (2D) camera 2020 to capture images of the object 2020 which include at least one alignment feature on the surface of the object. The camera 2020 may be a television camera or any camera capable of capturing a sequence of images.

2. A slice scanner 2010 that measures at least the distance to the surface 2070 of the object and the distance to interior features contained within a series of slice scans (such as an OCT, CAT, Ultrasound, MRI, Sonar, Radar, or similar device).

3. The scanner is controlled 2040 such that scan slices 2060 must intersect in such a way as to create a path to travel from any single scan slice to any other scan slice.

4. A determined spatial relationship exists between the camera 2020 and slice scan device 2010. The spatial relationship may be fixed, such as by mounting the camera and the scanner on the same platform, or may change over time, as long as this special relationship is known.

5. The time required to scan a single slice 2060 is sufficiently fast such that no motion blur occurs during a single scan slice acquisition time or the motion blur is significantly small such that it can be ignored over the slice scan time.

6. A programmed digital computer and/or signal processor 2050, to control the camera 2020 and scanner 2010, to process the images and scans, and to perform the steps explained below. The computer/DSP 2050 may include peripherals such as storage 2052 and a display 2054 or other components not shown.

Given the above requirements are satisfied, the method of 3D object reconstruction will be described with the aid of FIGS. 21A-21D for a slice scan device such as an OCT, CAT, Ultrasound, MRI, Sonar, Radar, or similar device.

Step 1 (FIG. 21A): Take at least two slice scans, slice 1 (2110-1) and slice P (2110-P), and also capture a corresponding image of the surface with the camera; simultaneously with each scan store the slice scan and camera image data for further processing. Select at least 1 feature (2120-1, 2120-P) in the surface image of the object to be used for x and y alignment of the scanned image slices. This example uses the + sign as the alignment feature.

Step 2A (FIG. 21A): Move one surface image relative to the other surface image in x and y to overlay the alignment feature (2120-1, 2120-P). Also move the image slices relative to one another by an identical amount as the surface images, to maintain at all times the precise spatial relationship between the alignment feature on the surface image and each image slice.

Step 2B (FIG. 21B): Identify the set of scanned x, y data points and coordinates that lie on the top surface edge of each image slice. These points will be present in the surface image of the object. These are the surface points that are closest to the imaging camera. Mathematically connect the individual scanned surface data points in each slice as shown by the bold line on the top surface of the object.

Locate the x, y data point on the top edge of each image slice that is common to both slices. This is the x, y coordinate where the two slices intersect in the x-y plane, as indicated by dashed vertical line V1.

Step 3: (FIG. 10B) Since the common x, y data point lies on the surface of the object, and since there is one and only one top surface, both slices 2110-1, 2110-P must share the same surface z value relative to the object at the surface x, y coordinate where the two slices intersect. Therefore image slice P can be connected to image slice 1 at this surface x, y, z coordinate as shown in FIG. 10B.

Step 4: (FIG. 10C) For each subsequent image slice 2110-N scanned of the object, (referred to as slice N) the alignment feature 2120-N is located in the accompanying surface image. This is true whether the object is stationary or in motion.

Step 5 (FIG. 21D): Mathematically connect the individual sampled surface data points along the top edge of Slice N using the same procedure described in step 2B.

Find which previously processed image slice number, referred to as slice P that slice N intersects. Locate the x, y coordinate that is common to scan slice N and P. This is the x, y coordinate where the two scans intersect in the x-y plane, as indicated by dashed vertical line V2.

Step 6 (FIG. 21D): At the intersecting x, y coordinate common to slices N and P (indicted by vertical line V2 in step 5) connect the two slices at the point where their top surface edges intersect such that the top edge of slice N will now have the same x, y, z coordinate value as the top edge of slice P at the intersection.

Continue operations in Steps 4, 5, and 6 until all intersecting slices have been connected where their top surface edges intersect, and construct the 3D model from the connected slices.

While FIGS. 21A to 21D illustrate the use of one alignment feature in the camera image to spatially align the object in the x,y camera plane between two slice scans. In some applications, it may be necessary or useful to use multiple features on the object surface to achieve alignment, especially if a single feature is not present in all camera images due to object motion. For slice scan application multiple features can be used to align the object in the x,y camera image plane in a similar fashion as previously explained for line scan applications. As previously explained; FIGS. 11A, 11B, and 11C show how multiple alignment features between consecutive images can be used for alignment. As an example, the “+” (plus sign) is only present in the camera image of FIG. 11A. Both the “+” sign and “*” (asterisk) are present in camera image 11B and only the “*” asterisk is present in camera image of 11C. In this example the “+” sign can be used to align FIG. 11A to FIG. 11B and the “*” asterisk can be used to align FIG. 11b to 11c . This by default aligns FIG. 11A to FIG. 11C even though they do not share a common feature. In this manner all images and scans can be aligned to each other in the x,y camera image plane. In general any one image can be aligned to any other image either by directly containing a feature common to both images or by finding pairs of common features in intermediate images linking the photos together as shown in FIGS. 11A, 11B, and 11C.

Compensating for Changes in Object Orientation Between Slice Scans

While FIG. 21 illustrates how to compensate for object motion in all three XYZ directions that occurs between acquisition of the slice scans, Fig. Sets 22 and 23 illustrate how to correct for changes in object orientation that occur between acquisition of the slice scans.

To determine whether object orientation correction is needed, initial XY feature alignment success is evaluated as a function of percentage of pixels matched after translation. If the object has not undergone significant rotation, the pixel match percentage will exceed a programmed threshold. If the object has undergone significant rotation, the pixel match percentage will be lower than the programmed threshold, indicating that object orientation correction is needed.

To correct for the change in orientation of an object, such as an eye for example, in an image acquired by a single OCT image slice can only be accurately characterized if it occurs in the axis perpendicular to the plane of the OCT image, as illustrated in FIG. 22A. Changes in orientation (gaze orientation for an eye) in other axes (out of the image plane) cannot be characterized as well. To measure change in orientation accurately, imagery must be provided to characterize all 3 axes of rotation, as illustrated in FIGS. 22B-1 to 22B-4. Planes A, B, and C can be constructed by having the TV camera image in FIG. 21 correspond to plane C and by having two temporally consecutive OCT slices spatially oriented either to be orthogonal to each other, as shown in FIG. 23A, or to intersect and not be parallel as shown in FIG. 23B, thereby creating two distinct axes to be viewed. In the example shown in FIGS. 22B-1 through 22B-4 and 22C, OCT image plane A sees rotation around the Y axis, OCT image plane B sees rotation around the X axis, and TV camera plane C sees rotation around the Z axis.

The two temporally consecutive OCT scans, referred to as a “scan set”, and the TV camera frame rate must be sufficiently fast to ensure the object does not move between acquisition of the three scan planes. As an example, in a preferred embodiment, using an OCT system manufactured by Thorlabs, model Vega, the orthogonal OCT scans for planes A and B in FIG. 22C each took twelve thousandths of a second for a total of 24 thousandths of a second (24 milliseconds) for one entire OCT scan set. During acquisition of each scan set, a TV camera (model Genie manufactured by DALSA, as an example) simultaneously acquires TV images at a frame rate of 24 milliseconds. Thus, imagery from all three axes are acquired during the same 24 millisecond period. The angular position of the scan set is sequentially incremented every 24 milliseconds to create a rotating scan pattern that covers the full 360 degrees of the eye or object as shown in FIG. 23C with a TV image taken for every scan set.

OCT scan sets shown in the preferred scan pattern of FIG. 23C may be acquired in the following sequential order: scan set 1 (1A then 1B), scan set 2 (2A then 2B), scan set 3 (3A then 3B), scan set 4 (4A then 4B). In this example scan 1A is 90 degrees (orthogonal) relative to scan 1B, scan 2A is 90 degrees relative to scan 2B, scan 3A is 90 degrees relative to scan 3B, and scan 4A is 90 degrees relative to scan 4B, etc., such that each set contains two perpendicular scans. It is important to emphasize that consecutive OCT slices can either be spatially oriented orthogonal to each other as shown in FIGS. 23A and 23C or intersect and not be parallel as shown in FIG. 23B, as long as the consecutive scans allow data from orthogonal axes to be extracted, even though the scan slices are not themselves orthogonal. In the case when the consecutive scans are orthogonal, as shown in FIG. 23C, the entire 360 degrees of the object being scanned can be covered by rotating the crosshair pattern of FIG. 23A a total of 90 degrees over the object. If the scans are not orthogonal more than a 90-degree rotation will be required to cover the entire 360 degrees.

FIG. 22D shows, as an example object, an eye with an initial orientation acquired at the beginning of data acquisition process, with orthogonal scans at 0 and 90 degrees. Assuming the object has moved during the acquisition process, FIG. 22G shows a different orientation acquired with orthogonal scans now positioned at 45 and 135 degrees. FIGS. 22E and 22H show the object orientation vectors corresponding to FIGS. 22D and 22G respectively. The object orientation vectors are perpendicular to the orientation planes created from the OCT data. In this example the distinct OCT iris data is used to create the orientation plane. FIG. 221 shows the difference in object orientation vectors between the two scan sets.

To correct for differences in object orientation, the object orientation vector for each scan set is calculated. The first scanned or most commonly occurring orientation is assumed to be the primary object orientation. Object orientation vectors with an angular difference (a as shown in FIG. 221) more than a predetermined threshold from the direction of the primary object orientation are corrected to point in the same direction as the primary object orientation as illustrated in FIG. 22J-1.

If the scanned object contains sufficient information to create an orientation plane, such as a distinct region that can be identified in each scan set, this can be used to facilitate measurement of orientation. Three-dimensional position data from this distinct region is extracted from the OCT scan sets and fit to a plane, referred to as the object orientation plane. A vector normal to this plane is computed and referred to as the object orientation vector. This is illustrated using a reference orientation plane made from the iris, as shown in FIGS. 22E and 22H.

If the scanned object does not have distinct regions or features that can be used to create an orientation plane, a target can be attached to the object being scanned to enable creation of an object orientation plane as shown in FIGS. 23D-1 to 23D-3. This target must be made of a material, such as thin glass or plastic as an example, to allow both the target and object to be captured in the OCT images. The target must be rigid and placed to ensure that it is imaged by both OCT slices in each scan set and undergoes the same motion and rotation as the object.

Furthermore, if the target lies between the TV camera and object being scanned, the target can either include an alignment feature, as shown in FIG. 23D-1, or be made of a transparent material, such as glass or plastic, as an example, to enable the TV camera to see an alignment feature on the object, as shown in FIG. 23D-2. To provide even more features for alignment, features can be placed on both the target and object as also shown in FIG. 23D-2.

While the target can be placed between the TV camera and object, it can also be placed behind the object as long as it lies within the field scanned by the OCT scan sets, as shown in FIG. 23D-3.

Once all object orientation vectors point in the same direction, the alignment features, as indicated in FIG. 21, must be in the same relative rotational position on the orientation plane to perform XYZ alignment. If the angular difference in rotational position (β in FIG. 22J) differs by more than a predetermined threshold, a correction is performed. This is implemented by first calculating the rotation vector, Rv in FIG. 22J-1, between the alignment feature for each scan set and a distinct feature. The first scanned or most commonly occurring rotation vector is assumed to be the reference rotation vector, referred to as RVref in FIG. 22J. Rotation vectors (RV) with an angular difference (β as shown in FIG. 22J) more than a predetermined threshold from reference rotation vector (RVref) are rotated to point in the same direction as the reference rotation vector (RVref). In the general case, rotation vector correction must be performed with respect to the same distinct second feature on the orientation plane by rotating around a vector perpendicular to the orientation plane and projected from this distinct second feature. As an example, for the eye it is convenient to select the center of the iris (pupil of the eye) as this distinct second feature, as shown in FIGS. 22J-1 and 22J-2. For other objects, any region or feature with distinct data can be used.

Once all rotation vectors are within a predetermined tolerance and all object orientation vectors are within a predetermined different tolerance, XYZ alignment is performed as illustrated in FIG. 21.

Implementation 3

Three Dimensional Reconstruction and Elimination of Motion Blur without the Use of a Camera and from Slice Scans of Structures which could be within an Object

This slice scan 3D reconstruction method does not require a camera and can still reconstruct structures deep within an object such as a tumor within a body. FIG. 24 is representative of this type scanner 2200. But unlike implementation 2, which only requires that each image slice intersect at least one other slice, this implementation requires that each image slice 2210 intersect at least two other image slices.

Requirements for three dimensional reconstruction and elimination of motion blur without the use of a camera and from slice scans of structures which could be within an object are as follows:

1. Each image slice intersects at least two other image slices.

2. The image slices must intersect not only when the object 2270 in FIG. 24 is stationary but also if the object moves within a defined spatial region.

3. The image slices must intersect in such a way as to create a path to travel from any single image slice to any other image slice

4. Intersecting slices must share a common set of Intensity values along the line of intersection; referred to as the Common Intensity Vector.

5. The time required to scan a single slice is sufficiently fast such that no motion blur occurs during a single scan slice acquisition time or the motion blur is significantly small such that it can be ignored over the slice scan time.

6. A programmable digital computer or digital signal processor 2230 in FIG. 24 controls the slice scanner 2220 and processes its output, for display 2250 and/or storage 2260.

Given the above requirements are satisfied, this method of 3D object reconstruction will be described with the aid of FIGS. 24 through 34 for a slice scan device 2220 such as an OCT, CAT, Ultrasound, MRI, Sonar, Radar, or similar device. Such devices can scan the interior of objects as illustrated in FIG. 24.

FIG. 25A is an example of a radial scan pattern with intersecting scan slices. Each scan creates an image slice scan representing a two dimensional image “slice” of the object. The position of each scan slice 2210 in FIG. 24 or slice 2301, 2302, or 2303 in FIG. 25A is relative to the framework 2280 of the scanning machine and does not know where the object 2260 is positioned within the scanning field 2280. The position of the image slice corresponds to where on the object the scan slice was actually scanned, with or without object motion. Assuming that there is no object motion the relative position of each image slice 2301, 2302, 2303 with respect to the object 2300 (i.e. where each image slice was actually taken on the object at the time of scan) would look like the mechanical scan pattern shown in FIG. 25A. However, if the object moved as the different scan slices were being acquired the image slices would not intersect the object at the expected positions shown in FIG. 25A, but instead would intersect the object at the positions shown in FIG. 25B. It is important to emphasize that if the object 2300 moves during the scan, such motion is unknown, subsequently without correction, these image slices will be positioned incorrectly.

FIG. 25A thus shows the physical scan pattern and where we expect the image slices to be positioned relative to each other assuming no object motion.

FIG. 25B shows where on the object the image slices were actually acquired because the object 2300 moved while it was being scanned.

The goal of the reconstruction algorithm is to shift the scanned image slices from their expected stationary position, which does not know about object motion, to the precise location that was actually scanned on the object as it moved. Essential to the reconstruction process is the realization that intersecting image slices must share a common set of intensity values in x, y, and z along the line of intersection, as shown in FIG. 25C.

Intersecting slices must share a common set of Intensity values along the line of intersection; referred to as the Common Intensity Vector. In the example of FIG. 25C:

I₁₂=common shared intensity values between image slice 1 (2401) and image slice 2 (2402)

I₁₃=common shared intensity values between image slice 1 (2401) and image slice 3 (2403)

I₂₃=common shared intensity values between image slice 2 (2402) and image slice 3 (2403)

In some applications, the Common Intensity Vector may be composed of a large percentage of matching data points rather than requiring a 100 percent match.

For simplicity, it may be easier to explain the reconstruction process in a two dimensional space rather than in three dimensions. With this in mind FIG. 26B shows the two dimensional top view of the three dimensional model shown in FIG. 26A and the dashed lines in FIG. 26B define a spatial region 2510 over which the image slices must intersect to enable object reconstruction.

To make the reconstruction example more realistic let us begin by assuming that Image Slice 2 2402 contains two additional Intensity Vectors that have the same set of values as Common Intensity Vector I₂₃. The position of these duplicates I₂₃ are shown in FIG. 27.

The 3D reconstruction proceeds as follows:

Step 1: For each image slice 2401, 2402, 2403 find and record possible intersecting image slices within the allowable search region by looking for all matching Intensity Vectors between pairs of image slices. Record the position of the matching Intensity Vectors within each slice.

At this step in the reconstruction process how the slices are connected is not known.

What is known at this step is that there exist three slices with the Intensity Vector positions as shown in FIGS. 28, 29 and 30.

At this step in the reconstruction process, the 123 vector position which represents the correct intersection of image slice 2 and image slice 3 is unknown.

Step 2: Reassemble the image slices and create the final model of the object such that:

1. Each pair of intersecting image slices in the scanned data must intersect in the reconstructed final model.

2. Slices in the final model must intersect such that all Intensity Vector positions within each slice are preserved and specifically the positions of the Common Vectors shared between intersecting slices.

3. If two image slices share a Common Intensity Vector and the Common Intensity Vector appears in duplicate positions within an image slice, only one of these positions can be used as the intersection between the image slices in the final model.

4. If a pair of image slices share at least two different value Common Intensity Vectors (which is not the same as the identical value in different positions of the slice), only one Common Intensity Value can be used in the final model.

5. When two intersecting image slices are connected along the line specified by a Common Vector, the intensity values in image slice 1 (2401) are aligned with the values in image slice 2 (2402) which thereby aligns the two slices in the z height axis.

The rules in Step 2 are then applied to construct the final model as follows:

1. Image slices 1 and 2 are joined at Common Vector I₁₂—see FIG. 31.

2. There are 3 possible positions that image slice 3 (2403 in FIG. 30) can intersect image slice 2 (2402 in FIG. 29). These 3 positions are shown by dashed lines 3201, 3202 3203 in FIG. 32. The positions are: I₂₃₋₁, I₂₃₋₂, I₂₃₋₃. According to Step 2 only one of these three possible positions can be used in the final model and all intersecting slices in the acquired data must be used in the final model. To determine the correct intersection: Image Slice 3 (2403 in FIG. 30) is first joined to slice 1 in FIG. 28 at Common Vector I₁₃. We know from the image slice acquired from the scan that the distance between Vector I₁₃ and I₂₃ in slice 3 is D₃ as shown in FIG. 30. If we draw a circle 3200 of radius D₃ around vector I₁₃, as shown in FIG. 32, we see that I₂₃₋₁ is the only position that can be connected, and is therefore chosen as the correct intersection of image slices 2 and 3. In other words, only I₂₃₋₁ in image slice 2 (2402 in FIG. 32) can intersect image slice 3 (3202 in FIG. 32) for the distance between I₁₃ and I₂₃ equal to D₃. See FIG. 33. In general, as the number of intersecting image scans and corresponding image slices increase, this imposes more spatial constraints which helps lock in the correct placement of all the slices.

While the example above compensates for translational motion, the algorithm can also compensate for rotational motion by searching for Common Vectors along diagonals. Furthermore, additional system and object information can be extrapolated from the measured parameters to indicate where to begin looking for the slice intersections.

This information includes:

1. Knowledge of the system mechanical slice scan angle and mechanical slice position, as shown in FIGS. 34A and 34B.

2. Maximum rate of object motion between slices

Assuming:

-   -   i. The first scan of the object is image slice 1 (scan slice         1=image slice 1) and consecutive temporal and spatial slices of         the object are taken every N seconds; and     -   ii. The maximum rate of object motion is M millimeters/second.

Then the maximum search area for temporal slice 2 relative to scan slice 1 is a sphere of radius M mm/sec×Nsec.

b. For each consecutive scan, knowledge of the physical scan angle and scan position (FIG. 34A) combined with the maximum rate of object motion can be used to limit the search area (between the inner radius circle 3410 and outer circle 3210). (Is this correct?)

It is important to emphasize that once the first few image slices (3 or 4 for example) have been connected at their correct Common Vector positions, this information limits the search area 3410 for the next consecutively acquired image slice.

Another major feature of this reconstruction algorithm is that in the event that motion blur occurs during acquisition of a slice scan, this blur can be reduced or eliminated. This can be accomplished by reducing the area and time of each scan and increasing the total number of intersecting slices scanned within the object. In other words, the size of each scan slice is adjusted to eliminate or minimize motion blur over the scan slice acquisition time. As an example: FIG. 34C illustrates how the larger region scanned by slice 3450 can be scanned by the 4 smaller intersecting regions 34100, 34200, 34300, and 34400. In this example the time required to scan each of the smaller regions is approximately one quarter the time required to scan larger region 3450, which thereby reduces any motion blur.

Combining Three Dimensional Sub-Sections to Create the Entire Model

To obtain access to all object surfaces it is frequently necessary to scan an object from different angles 3501, 3502, 3503 as shown in FIG. 35.

As an example, to create a three dimensional model of the eye which includes the regions of the sclera normally covered by the eyelids it is often necessary to combine multiple scans taken with the eye 3600 at different orientations or gazes (3610, 3620, 3630, 3640, 3650, 3660) as shown in FIG. 36. An algorithm for stitching together sub-sections of the same object taken at different orientations or different gazes to create an entire 3D model is now described in connection with FIGS. 37 through 42.

FIG. 37 is the head on view of an object with two alignment features shown as two dots 3701, 3702.

To stitch together multiple sub sections—perform the following steps:

Step 1 (FIG. 37): Given two 3D sections from the same object that are to be aligned: Select at least two alignment features (3701, 3702) present in the camera image of each 3D section and find the x,y centroid of each feature in each camera image. Each camera image must contain both alignment features as illustrated in FIG. 37. Use the x,y centroid position to extract its z position from the 3D model section.

Step 2 (FIG. 38): Locate the corresponding alignment features in both 3D sections of the object. In FIG. 38, the dashed lines with hollow dots show object section 1, and the solid lines with solid dots show rotated object section 2.

Vector A (3801) of Length=D1 and Vector B (3802) of Length=D2 represent the difference in position of the same alignment features between the two sections. i(Note also that in this example that D1>D2).

Step 3 a (FIG. 39): Prepare for Translation

FIG. 39 shows the spatial relationship prior to translation. In FIG. 39, the dashed lines with hollow dots show object section 1, and the solid lines with solid dots show rotated 1 object section 2.

In this step, we select Vector A (3901) for translation of the first alignment feature. We then translate the model of section 2 along Vector A such that Feature 1R (3902) has the same X,Y,Z Coordinate as Feature 1.

Step 3 b (FIG. 40): This figure shows the spatial relationship of the two models after the translation operation described in Step 3 a.

In FIG. 40, the dashed line image 4001 is the original position of 3D object section 1.

The solid lines 4002 represent the position of the rotated object section after Feature 1R (3902 in FIG. 39) in 3D object section 2 has been superimposed over Feature 1 in 3D object section 1 of FIG. 40.

Step 4 a (FIG. 41): Prepare for Rotation Correction.

FIG. 41 shows the Solid Line object section 4101 including aligned feature 4003 prior to rotation.

To correct for rotation, we define Vector 1 as the line that passes through the two similar but unaligned Features 2 (4112) and 2R (4112R) in the X,Y Plane.

We then define Vector 2 as the line that passes through the aligned feature 4003 in X, Y and Z and is perpendicular to Vector 1.

To correct for rotation, we rotate the entire model (solid lines) around Vector 2 until the two unaligned features 2 and 2R (4112 and 4112R, respectfully) share the same X, Y, Z position.

Step 4 b (FIG. 42): Perform the rotation operation described in Step 4 a.

FIG. 42 shows Feature 1 superimposed over feature 1R (4003) and Feature 2 superimposed (4203) over Feature 2R.

After the Rotation Operation is complete, all features and the two models are aligned in X, Y and Z, as shown in FIG. 42.

While FIGS. 37 through 42 describe how to stitch together subsections of a three-dimensional object using features solely on the surface, FIGS. 43A through 43G describe how features either on the surface or within the object can be used for stitching.

To enable stitching of multiple subsections, an orientation plane must be created for each subsection. This requires that the same distinct region be identified in the subsections being combined. Three-dimensional position data from each distinct region is extracted from the three-dimensional data of each subsection and fit to a plane, referred to as the subsection orientation plane. Vectors normal to each plane are computed and referred to as the subsection orientation vectors. As an example, for two subsections of an eye, this is illustrated using orientation planes made from the iris, as shown in FIGS. 43A to 43D.

The orientation vector for each subsection is calculated. The orientation vector of the subsection which overlaps the most with other subsections is assumed to be the primary object orientation. Subsection orientation vectors with an angular difference (θ as shown in FIG. 43E) more than a predetermined threshold from the direction of the primary object orientation are corrected to point in the same direction as the primary object orientation as illustrated in FIG. 43F.

Once all subsection orientation vectors point in the same direction, the alignment features, which can be on the surface of the object as indicated in FIG. 37, or interior to the object, such as for example on the iris as illustrated in FIG. 43A, must be in the same relative rotational position on the orientation plane of each subsection to stitch the subsections together. If the angular difference in rotational position (Ω in FIG. 43F) differs by more than a predetermined threshold, a correction is performed. This is implemented by first calculating the subsection rotation vector, Rv in FIG. 43F, between the alignment feature and a distinct second feature for each subsection. The rotation vector associated with the subsection containing the primary object orientation plane is assumed to be the reference rotation vector, referred to as RVref in FIGS. 41F and 41G. Rotation vectors (SRV) with an angular difference (Ω as shown in FIG. 43F) more than a predetermined threshold from reference rotation vector (RVref) are rotated to point in the same direction as the reference rotation vector (RVref). In the general case, rotation vector correction must be performed with respect to the same distinct second feature on the orientation plane by rotating around a vector perpendicular to the orientation plane and projected from this distinct second feature.

As an example, for the eye it is convenient to select the center of the iris (pupil of the eye) as this distinct second feature, as shown in FIGS. 43F and 43G. For other objects, any region or feature with distinct data can be used.

Once all subsection rotation vectors are within a predetermined tolerance and all subsection orientation vectors are within a predetermined different tolerance, the subsections need to be translated in X, Y, and Z directions to be stitched together. This is accomplished by translating subsections to each other by moving common features into the same three-dimensional position in space, as illustrated in FIG. 43H.

An improvement possible with the techniques described herein became evident with a simple test. First, a spherical object having a red dot as a reference feature was scanned using a line scanner. The sphere was moved away from the scanner in a direction perpendicular to the front of the scanner as the sequence of meridian scans were taken. The total distanced the sphere moved during the scan was 15 millimeters. The system was not given any information about the path of the sphere. FIG. 44A is the result when only the meridian scans are used; note the disarray of the resulting points.

Next, we scanned the same sphere with red dot again, but this time we also captured a picture simultaneously with each meridian scan. We also ran the line scan algorithm as described above in connection with FIGS. 10A-10D using the red dot as the reference feature. The system was again given no other information about the path of the sphere and again the total distance the sphere moved during the scan was 15 millimeters. FIG. 44B was the result—demonstrating a marked improvement in the ability to correctly detect the detailed shape of the sphere, even as the sphere is moving with respect to the camera.

3D Reconstruction of Anterior and Posterior Surfaces of the Cornea and/or Sclera

The above-described 3D modeling techniques can be extended to solve certain critical vision impairment cases. For example, there currently exist millions of patients with cataracts whose vision cannot be optimally corrected because previously performed refractive surgery has significantly altered the shape of their cornea.

These alterations may be to such an extent that existing instrumentation no longer provides accurate topological data, but instead only provides an inaccurate approximation. Without knowing the patient's specific eye shape there is no way of determining the correct Intracular Lens (IOL). As a result, approximations that are currently used frequently result in sub-optimal vision.

In a first scenario, OCT image slice scans of an eye such as those described in FIG. 19 can be processed to determine other high contrast boundaries. Thus, the image slice scans can be processed, using the 3D modelling techniques described above, to not only determine the “top surface” (that is the anterior corneal surface 1920, but to also determine the shape of the posterior corneal surface 1930. Having determined the shape of the anterior 1920 and posterior 1930 corneal surfaces, an accurate measurement of corneal thickness 1940 can also be estimated across the eye. This measurement of corneal thickness 1940 can then be factored into a power calculation when fabricating the vision correction optics for an IOL contact lens.

In other instances, a severe injury may have disrupted the expected regular shape of the cornea, sclera, or other parts of the eye. FIGS. 45A and 45B are example OCT slice scans of a cornea that has healed after being injured by exploding glass that penetrated the anterior and posterior corneal surfaces. Notice that the posterior corneal surface 4520 and resulting corneal thickness 4530 are highly irregular. Fortunately, the above-described OCT-based 3D modeler is capable of scanning the anterior 4510 and posterior 4520 corneal surfaces, the iris 4540, and the sclera 4550 (including steep regions of the sclera 4550 normally covered by the eyelids). For corneas like this that have been severely damaged, vision can often only be restored using a scleral lens. The data provided by the OCT scanner described above can thus be used for determining the bearing surface shape of a scaleral lens as well as optical power for the correcting optics.

However, when the 3D shape of the entire scleral region is required, for example to manufacture a large diameter scleral lens, complications arise when stitching together scans from scleral regions normally covered by the eyelids. When evaluating the capabilities of the 3D model under ideal conditions, by stitching together multiple scans from a calibration sphere (such as was described in connection with FIGS. 44A and 44B), maximum errors on the order of 50 microns are achievable over the entire calibration surface.

However, when stitching together multiple scans of an actual human eye, additional errors are introduced by features such as the bulbar conjunctiva which contain blood vessels and is a viscous material that can change thickness and shape as the eye rotates. The measurement signal emitted from the scanner sometimes reflects off a blood vessel and sometimes off a thinner region of the conjunctiva. This gives rise to uncertainties in conjunctival thickness on the order of 50 microns or approximately one degree at the limbus of the eye. As the scanning optics moves away from the limbus toward the edges of a scleral lens this error increases. For an 18 mm diameter scleral lens, this one degree of angular error at the limbus gives rise to a fitting error of approximately 150 microns at the scleral lens perimeter. This error is illustrated in FIG. 46 showing laser scan 1 taken of the central corneal region stitched together with laser scan 2 taken with the eye positioned to expose the region behind the eyelid.

To solve this stitching problem, the 3D model may be derived by using both surface and interior data to stitch images together. In one example evident from FIG. 45A, data from the anterior 4510 and posterior 4520 corneal surfaces, the edges of the iris 4540, top edge of the crystalline lens 4560 and/or sclera 4550 may be used to provide points for stitching. Using multiple stitch points separated over a larger distance of the eye produce significantly superior stitching accuracy. FIG. 45B shows the ability of the system to measure steep curvature of the sclera 4550 (e.g., “below” the iris in the view of FIG. 45B) even for z-axis scans not going through the center of the pupil (the distance from the highest point on the cornea to the sclera in this example was 11 mm).

CONCLUSION

While this invention has now been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made, without departing from the scope of the invention encompassed by the appended claims. 

The invention claimed is:
 1. A method for generating a three dimensional representation of an object, the object or a target attached to the object, jointly having at least a distinct first and second visual feature, the method comprising: A) directing a camera along an imaging axis for generating a sequence of two-dimensional images of the object in which each camera image contains at least two distinct visual features, B) directing a scanner for generating scans of the object, each scan including a plurality of sampled measurements of the object, C) maintaining a determined spatial relationship between the scanner and the camera, D) controlling the scanner to produce a plurality of scan sets, with each scan set allowing data from orthogonal axes to be extracted, each scan set including a plurality of sampled measurements, each of the scan sets intersecting at least one other scan set in an image plane observed by the camera, to thereby create a path from any single scan set to any other scan set in the image plane observed by the camera, E) capturing an image of the object including at least the two distinct visual features from the camera for each of the plurality of scan sets, thereby capturing a corresponding camera image for each scan set, F) ensuring that any one camera image can be aligned to at least one other camera image either by sharing at least one distinct visual feature directly in common with another camera image or by sharing at least one distinct visual feature in common with an intermediate camera image which has been aligned to a previous camera image, G) aligning the camera images captured with each scan set, by overlaying the selected distinct visual feature shared by the camera images, and by measuring alignment success of the overlaying by a percentage of matching pixels comprising the distinct visual feature, such that when the percentage of matching pixels equals or exceeds a predetermined XY alignment threshold, feature alignment is said to be successful, but when the percentage of matching pixels is less than the predetermined XY alignment threshold, then performing an orientation correction by: extracting an orientation plane from each scan set and for each orientation plane, creating a vector that is normal to that orientation plane, referred to as the orientation plane's object orientation vector, a. for at least one pair of two different scan sets, i. locating the distinct first visual feature in the camera image associated with each scan set, such that both scan sets share the same distinct first visual feature, and extracting the three-dimensional XYZ position of this distinct first visual feature centroid from its corresponding scan set data, ii. rotating at least one object orientation vector with its corresponding scan set around the distinct first feature centroid, so that the two object orientation vectors are parallel and point in the same direction in three-dimensional space to within a predetermined orientation threshold, thereby compensating for changes in orientation of the object between scan sets, iii. locating the distinct second visual feature in the camera image associated with each scan set, such that both scan sets share the same distinct second visual feature, and extracting the three-dimensional XYZ position of this distinct second visual feature centroid from its corresponding scan set data, iv. projecting each object orientation vector out of its orientation plane from the distinct second visual feature centroid, v. for each scan set, creating a vector starting at the distinct second visual feature centroid, pointing toward the distinct first visual feature centroid, referred to as the rotation vector, vi. rotating at least one scan set around its object orientation vector until both rotation vectors are parallel and point in the same direction within a predetermined rotation threshold, and vii. aligning the first distinct visual features shared by the scan sets by overlaying them in the camera plane via translation through three-dimensional space, to thereby also align the corresponding scan sets by a corresponding amount, H) extracting the surface of the object from each slice within each scan set, and connecting the individual sampled surface measurements in each slice to produce a continuous representation of the object surface referred to as object surface lines i. for at least one set of two object surface lines, a. locating a point in the camera image plane where the continuous representations of the two surface lines cross, and b. moving at least one surface line with its corresponding slice scan along the imaging axis in a plane perpendicular to the camera image plane so that the two continuous surface line representations intersect at a common point in three dimensional space, thereby compensating for movement of the object between successive slice scans of the scanner.
 2. The method of claim 1 additionally comprising: generating the three dimensional representation of the object from two or more scan sets.
 3. The method of claim 1 wherein the captured image is a two-dimensional image of a surface of the object containing at least two distinct visual features.
 4. The method of claim 1 wherein the scanner is a two or three dimensional scanner and the sampled measurements each correspond to the intensity of a returned signal from an object measured at a specific x,y or x,y,z coordinate in the scanner.
 5. The method of claim 1 wherein the slice scanner is one of an OCT, CAT, Ultrasound, MRI, sonar, or radar.
 6. The method of claim 1 wherein a time to acquire the sampled measurements is sufficiently small such that motion blur occurring during the sampling of a scan set can be ignored.
 7. A method for generating a three dimensional representation of an object, the object or a target attached to the object, jointly having at least a distinct first and second visual feature, the method comprising: A) generating a plurality of three-dimensionally accurate subsections of the same object, such that each subsection overlaps at least one other subsection of the object, and the subsections also overlapping such that a continuous path exists from each subsection to any other subsection, B) extracting an orientation plane from each subsection, referred to as a subsection orientation plane, and creating a vector normal to the subsection orientation plane referred to as a subsection orientation vector, a. for at least one pair of two overlapping subsections, i. locating the distinct first visual feature in the camera image associated with each subsection, such that both subsections share the same distinct first visual feature, and extracting the three-dimensional XYZ position of this distinct first visual feature centroid from its corresponding subsection data, ii. rotating at least one subsection orientation vector with its corresponding subsection data around the distinct first visual feature centroid, so that the two subsection orientation vectors are parallel and point in the same direction in three-dimensional space to within a predetermined orientation threshold, thereby compensating for changes in orientation of the object between subsections, iii. locating the distinct second visual feature in the camera image associated with each subsection, such that both subsections share the same distinct second visual feature, and extracting the three-dimensional XYZ position of this distinct second visual feature centroid from its corresponding subsection data, iv. projecting each subsection orientation vector out of its subsection orientation plane from the distinct second visual feature centroid, v. for each subsection, creating a vector starting at the distinct second visual feature centroid, pointing toward the distinct first visual feature centroid, referred to as the subsection rotation vector, vi. rotating at least one subsection around its subsection orientation vector until both subsection rotation vectors are parallel and point in the same direction within a predetermined rotation threshold vii. aligning the distinct first visual features shared by the subsections by overlaying them in the camera plane via translation in the camera image plane through three-dimensional space, to thereby also align the corresponding subsections by a corresponding amount, and viii. aligning the first distinct visual features by moving at least one first distinct visual feature with its corresponding subsection data along the imaging axis in a plane perpendicular to the camera image plane so that the two distinct visual features occupy the same point in three dimensional space, thereby stitching the subsections of the object together.
 8. The method of claim 7 additionally comprising: generating the three dimensional representation of the object from two or more subsections.
 9. The method of claim 4 wherein the object is an eye and wherein: the sampled measurements include distances to two or more features of the eye including an anterior corneal surface, posterior corneal surface, corneal thickness, iris, crystalline lens, or sclera. 